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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformations and DataFrame Operations	35%	- Window functions - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Persisting transformed data - Using built-in functions
Topic 2: Performance Optimization and Best Practices	20%	- Warehouse sizing for Snowpark - Debugging and explain plans - Caching strategies - Vectorized UDFs - Query pushdown and optimization - Minimizing data transfer
Topic 3: Snowpark Concepts	15%	- Stored procedures and conditional logic - Snowpark architecture and core concepts - Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Transformations vs. Actions - Snowpark Sessions and connection management
Topic 4: Snowpark API for Python	30%	- User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation - Establishing connections and session management - Reading and writing data - Working with Semi-structured data

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q113-Q118):

NEW QUESTION # 113
You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application's performance is currently bottlenecked by the data transfer between Snowflake and the client machine running the Python code. Which of the following strategies can effectively minimize data transfer and improve performance?

A. Use Snowpark's lazy evaluation capabilities to defer data transfer until absolutely necessary, perform data filtering and aggregation within Snowflake before bringing data to the client, and utilize vectorized UDFs for performance-critical operations.
B. Increase the number of worker nodes in the Snowflake virtual warehouse to improve data processing speed.
C. Convert all Python UDFs to SQL UDFs to leverage Snowflake's query optimizer.
D. Fetch the entire dataset into a Pandas DataFrame on the client machine and perform all data transformations locally.
E. Disable Snowpark's lazy evaluation to ensure that all data is immediately transferred to the client for processing.

Answer: A

Explanation:
Snowpark's lazy evaluation, data filtering/aggregation within Snowflake, and vectorized UDFs are crucial for minimizing data transfer. Lazy evaluation ensures that data is only transferred when needed. Pushing down operations like filtering and aggregation to Snowflake reduces the amount of data transferred. Vectorized UDFs allow UDFs to operate on batches of data, improving performance. Fetching the entire dataset into a Pandas DataFrame (C) exacerbates the data transfer bottleneck. Disabling lazy evaluation (D) is counterproductive.

NEW QUESTION # 114
A data engineer has developed a Snowpark Python stored procedure, 'calculate daily summary', that processes sales data and generates a daily summary table. The procedure takes a date string as input and writes the summary to a table named 'DAILY SALES SUMMARY'. The engineer needs to operationalize this stored procedure to run automatically every day at midnight. Which of the following approaches is the MOST efficient and reliable way to schedule and execute this Snowpark stored procedure in Snowflake?

A. Develop a Snowpark UDF that wraps the stored procedure call and create a scheduled job in a third-party orchestration tool (e.g., Airflow, Prefect) to call the UDF.
B. Use the Snowflake web interface to manually execute the stored procedure every day at midnight.
C. Create a Python script outside of Snowflake that uses the Snowflake Connector for Python to connect to Snowflake and execute the stored procedure using and schedule it using a system-level scheduler (e.g., cron or Windows Task Scheduler).
D. Create a Snowflake Task that calls the stored procedure with a cron schedule that triggers the task every day at midnight. Ensure the task has a warehouse associated with it.
E. Use a Snowpipe to trigger the stored procedure when new data arrives in the staging area.

Answer: D

Explanation:
Snowflake Tasks provide a native and reliable way to schedule and execute stored procedures directly within Snowflake. Using a task eliminates the need for external schedulers and dependencies. Option A is manual and not scalable. Option B introduces external dependencies and potential security concerns. Option D adds an unnecessary layer of complexity with the UDF. Option E is triggered by data arrival and not a scheduled timeframe, also not intended for direct execution of stored procedures. Snowflake tasks are designed specifically for this purpose.

NEW QUESTION # 115
You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

Answer: A

Explanation:
Option A is correct. It correctly uses to group by 'product_id' and 'order_by' to sort by 'sale_date' within each product group. It then calculates the cumulative sum using Options B, C, D and E contain typos or incorrect function usage or order of arguments. 'cumulative_surn' is not a standard function provided.

NEW QUESTION # 116
You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:

Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)

Answer: B,C,D,E

Explanation:
Options A, B, C, and D represent correctly written pytest tests. A tests a valid discount, B tests the exception handling for an invalid percentage, C tests the case with no discount, and D tests a full discount. E is incorrect because the calculate_discount function does not explicitly check for negative prices, therefore 'pytest.raises(ValueErrorV will cause a failed test since no ValueError is raised.

NEW QUESTION # 117
Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

A. Using username/password authentication is the most secure and recommended approach for production environments.
B. Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.
C. OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
D. OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
E. Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.

Answer: B,D,E

Explanation:
Options B, C, and D are correct. OAuth provides a secure delegation mechanism. Key pair authentication is also a secure method, eliminating password storage in code. Snowflake Browser-Based MFA relies on web browser redirection. Option A is incorrect because username/password is generally less secure than key pair or OAuth. Option E is incorrect because External Oauth providers like Okta are Azure AD is supported for authentication using a federated OAuth flow.

NEW QUESTION # 118
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